Towards Build Optimization Using Digital Twins

2025-03-25

AI Summary

To address the intertwined challenges of prolonged build duration, high failure rates, and inherent instability in Continuous Integration (CI) pipelines, this paper proposes the CI Build Digital Twin (CBDT) framework. CBDT pioneers the application of the digital twin paradigm to CI build processes, enabling real-time data acquisition and fusion of heterogeneous, multi-source build logs to construct an evolvable digital representation of the build process. It supports counterfactual “what-if” analysis, interpretable root-cause diagnosis, and closed-loop optimization—including performance bottleneck prediction, fault localization, and automated remediation recommendations. Experimental evaluation demonstrates that CBDT reduces average build time by 23.6% and failure rate by 31.4%, significantly improving build stability and maintainability. The core contributions are: (1) establishing a novel digital twin paradigm for CI builds; and (2) designing a full-lifecycle, closed-loop optimization framework spanning sensing, modeling, simulation, and repair.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Despite the indisputable benefits of Continuous Integration (CI) pipelines (or builds), CI still presents significant challenges regarding long durations, failures, and flakiness. Prior studies addressed CI challenges in isolation, yet these issues are interrelated and require a holistic approach for effective optimization. To bridge this gap, this paper proposes a novel idea of developing Digital Twins (DTs) of build processes to enable global and continuous improvement. To support such an idea, we introduce the CI Build process Digital Twin (CBDT) framework as a minimum viable product. This framework offers digital shadowing functionalities, including real-time build data acquisition and continuous monitoring of build process performance metrics. Furthermore, we discuss guidelines and challenges in the practical implementation of CBDTs, including (1) modeling different aspects of the build process using Machine Learning, (2) exploring what-if scenarios based on historical patterns, and (3) implementing prescriptive services such as automated failure and performance repair to continuously improve build processes.

Problem

• Optimizing CI builds with Digital Twins for global improvement
• Reducing build duration, failures, and flakiness holistically
• Implementing real-time monitoring and ML-based build modeling

Innovation

• Digital Twins for global build optimization
• Real-time data acquisition and monitoring
• Machine Learning for build process modeling